Bibliographic record
Abstract
Abstract This chapter sets the context for the articles in the volume – explorations in the use of OKAY in a diverse set of languages, including American English, Brazilian Portuguese, Danish, Estonian, Finnish, French, German, Italian, Japanese, Korean, Mandarin, Polish, and Swedish. We first outline the origins of OKAY in American English and its spread to other languages as a loanword, motivating this study of OKAY. We then review the state of the art in research on OKAY in spoken interaction in a variety of settings. Since this volume makes a case for investigating OKAY empirically as it is actually used in particular occasions of spoken and embodied interaction, the review of existing work on OKAY will be connected to relevant developments in Conversation Analysis ( Sidnell 2010 ; Clift 2016 ) and Interactional Linguistics ( Couper-Kuhlen and Selting 2018 ). We close with a discussion of overarching themes and promising new research directions that emerge from existing work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.500 | 0.307 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".